from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2022-12-06 14:02:39.833873
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64('2020-12-27'),
'orange', 'inside top left'),
'Weihnachten 2020': (np.datetime64('2020-12-24'), np.datetime64('2020-12-27'),
'blue', 'inside top left'),
'3. Lockdown': (np.datetime64('2020-12-27'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 06, Dec, 2022
Time: 14:02:48
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -51.1384
Nobs: 862.000 HQIC: -51.4451
Log likelihood: 11336.7 FPE: 3.75903e-23
AIC: -51.6353 Det(Omega_mle): 3.38839e-23
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.294484 0.050076 5.881 0.000
L1.Burgenland 0.106478 0.034243 3.110 0.002
L1.Kärnten -0.107440 0.018368 -5.849 0.000
L1.Niederösterreich 0.215756 0.071931 2.999 0.003
L1.Oberösterreich 0.089352 0.068179 1.311 0.190
L1.Salzburg 0.249426 0.036373 6.858 0.000
L1.Steiermark 0.030002 0.047753 0.628 0.530
L1.Tirol 0.130014 0.038809 3.350 0.001
L1.Vorarlberg -0.063618 0.033409 -1.904 0.057
L1.Wien 0.059723 0.060966 0.980 0.327
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.064968 0.103166 0.630 0.529
L1.Burgenland -0.008945 0.070546 -0.127 0.899
L1.Kärnten 0.050496 0.037841 1.334 0.182
L1.Niederösterreich -0.175928 0.148191 -1.187 0.235
L1.Oberösterreich 0.369663 0.140462 2.632 0.008
L1.Salzburg 0.285730 0.074934 3.813 0.000
L1.Steiermark 0.110053 0.098380 1.119 0.263
L1.Tirol 0.311692 0.079954 3.898 0.000
L1.Vorarlberg 0.024915 0.068829 0.362 0.717
L1.Wien -0.026453 0.125600 -0.211 0.833
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.198362 0.025914 7.654 0.000
L1.Burgenland 0.090242 0.017721 5.093 0.000
L1.Kärnten -0.008625 0.009505 -0.907 0.364
L1.Niederösterreich 0.267095 0.037224 7.175 0.000
L1.Oberösterreich 0.116628 0.035283 3.306 0.001
L1.Salzburg 0.052419 0.018823 2.785 0.005
L1.Steiermark 0.016463 0.024712 0.666 0.505
L1.Tirol 0.099118 0.020084 4.935 0.000
L1.Vorarlberg 0.056152 0.017289 3.248 0.001
L1.Wien 0.113217 0.031550 3.589 0.000
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.105456 0.026588 3.966 0.000
L1.Burgenland 0.047640 0.018181 2.620 0.009
L1.Kärnten -0.016537 0.009752 -1.696 0.090
L1.Niederösterreich 0.195989 0.038192 5.132 0.000
L1.Oberösterreich 0.280710 0.036200 7.755 0.000
L1.Salzburg 0.118413 0.019312 6.132 0.000
L1.Steiermark 0.100847 0.025354 3.977 0.000
L1.Tirol 0.124412 0.020606 6.038 0.000
L1.Vorarlberg 0.069444 0.017739 3.915 0.000
L1.Wien -0.027260 0.032369 -0.842 0.400
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.130901 0.048123 2.720 0.007
L1.Burgenland -0.053591 0.032907 -1.629 0.103
L1.Kärnten -0.037277 0.017651 -2.112 0.035
L1.Niederösterreich 0.168889 0.069125 2.443 0.015
L1.Oberösterreich 0.134428 0.065520 2.052 0.040
L1.Salzburg 0.290655 0.034954 8.315 0.000
L1.Steiermark 0.034022 0.045890 0.741 0.458
L1.Tirol 0.162686 0.037295 4.362 0.000
L1.Vorarlberg 0.107249 0.032106 3.340 0.001
L1.Wien 0.063682 0.058587 1.087 0.277
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.059557 0.038103 1.563 0.118
L1.Burgenland 0.038389 0.026055 1.473 0.141
L1.Kärnten 0.049666 0.013976 3.554 0.000
L1.Niederösterreich 0.227028 0.054733 4.148 0.000
L1.Oberösterreich 0.273141 0.051878 5.265 0.000
L1.Salzburg 0.058350 0.027676 2.108 0.035
L1.Steiermark -0.006919 0.036336 -0.190 0.849
L1.Tirol 0.158207 0.029530 5.357 0.000
L1.Vorarlberg 0.068511 0.025421 2.695 0.007
L1.Wien 0.074732 0.046389 1.611 0.107
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.183801 0.045747 4.018 0.000
L1.Burgenland 0.016760 0.031282 0.536 0.592
L1.Kärnten -0.060212 0.016780 -3.588 0.000
L1.Niederösterreich -0.092903 0.065712 -1.414 0.157
L1.Oberösterreich 0.184606 0.062285 2.964 0.003
L1.Salzburg 0.058811 0.033228 1.770 0.077
L1.Steiermark 0.228861 0.043624 5.246 0.000
L1.Tirol 0.487358 0.035454 13.746 0.000
L1.Vorarlberg 0.051544 0.030521 1.689 0.091
L1.Wien -0.056634 0.055694 -1.017 0.309
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.158303 0.051952 3.047 0.002
L1.Burgenland 0.000087 0.035525 0.002 0.998
L1.Kärnten 0.066547 0.019056 3.492 0.000
L1.Niederösterreich 0.200147 0.074626 2.682 0.007
L1.Oberösterreich -0.068950 0.070734 -0.975 0.330
L1.Salzburg 0.220283 0.037735 5.838 0.000
L1.Steiermark 0.113053 0.049542 2.282 0.022
L1.Tirol 0.084170 0.040263 2.090 0.037
L1.Vorarlberg 0.123069 0.034661 3.551 0.000
L1.Wien 0.104924 0.063250 1.659 0.097
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.357584 0.030665 11.661 0.000
L1.Burgenland 0.006405 0.020969 0.305 0.760
L1.Kärnten -0.024322 0.011248 -2.162 0.031
L1.Niederösterreich 0.227037 0.044048 5.154 0.000
L1.Oberösterreich 0.158659 0.041751 3.800 0.000
L1.Salzburg 0.052146 0.022273 2.341 0.019
L1.Steiermark -0.015829 0.029243 -0.541 0.588
L1.Tirol 0.116831 0.023765 4.916 0.000
L1.Vorarlberg 0.072481 0.020459 3.543 0.000
L1.Wien 0.050448 0.037333 1.351 0.177
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.038039 0.156223 0.178995 0.166396 0.139290 0.122754 0.064558 0.219902
Kärnten 0.038039 1.000000 0.000567 0.131581 0.026540 0.098540 0.431668 -0.049864 0.102017
Niederösterreich 0.156223 0.000567 1.000000 0.343957 0.168529 0.310231 0.124469 0.190827 0.341644
Oberösterreich 0.178995 0.131581 0.343957 1.000000 0.233888 0.340026 0.176091 0.179193 0.274000
Salzburg 0.166396 0.026540 0.168529 0.233888 1.000000 0.151834 0.136108 0.152636 0.142395
Steiermark 0.139290 0.098540 0.310231 0.340026 0.151834 1.000000 0.156465 0.147569 0.093547
Tirol 0.122754 0.431668 0.124469 0.176091 0.136108 0.156465 1.000000 0.121119 0.167179
Vorarlberg 0.064558 -0.049864 0.190827 0.179193 0.152636 0.147569 0.121119 1.000000 0.020140
Wien 0.219902 0.102017 0.341644 0.274000 0.142395 0.093547 0.167179 0.020140 1.000000